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Co-localized memory and processing components are necessary for the creation of scalable neuromorphic hardware, and these components can be produced using inexpensive techniques. While memristors have emerged as promising candidates for synaptic emulation, most devices rely on complex fabrication processes that hinder large-scale deployment. This work presents a fully inkjet-printed memristor utilizing hexagonal boron nitride (hBN) and graphene inks, demonstrating a manufacturable approach to neuromorphic systems. The devices exhibit nonvolatile resistive switching with symmetric pinched hysteresis loops [Formula: see text] and tunable conductance states. Furthermore, a piece-wise nonlinear memristor model was developed and implemented in Simscape to enable system-level simulations. A [Formula: see text] reservoir computing architecture, constructed using these memristive devices, successfully demonstrated temporal pattern processing and feature extraction capabilities. The results establish printed memristors as viable building blocks for next-generation edge computing applications, combining the advantages of solution-processable materials with neuromorphic functionality.
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ECRAM Materials, Devices, Circuits and Architectures: A Perspective
Abstract Non‐von‐Neumann computing using neuromorphic systems based on two‐terminal resistive nonvolatile memory elements has emerged as a promising approach, but its full potential has not been realized due to the lack of materials and devices with the appropriate attributes. Unlike memristors, which require large write currents to drive phase transformations or filament growth, electrochemical random access memory (ECRAM) decouples the “write” and “read” operations using a “gate” electrode to tune the conductance state through charge‐transfer reactions, and every electron transferred through the external circuit in ECRAM corresponds to the migration of ≈1 ion used to store analogue information. Like static dopants in traditional semiconductors, electrochemically inserted ions modulate the conductivity by locally perturbing a host's electronic structure; however, ECRAM does so in a dynamic and reversible manner. The resulting change in conductance can span orders of magnitude, from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. In this in‐depth perspective, the history of ECRAM, the recent progress in devices spanning organic, inorganic, and 2D materials, circuits, architectures, the rich portfolio of challenging, fundamental questions, and how ECRAM can be harnessed to realize a new paradigm for low‐power neuromorphic computing are discussed.
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- Award ID(s):
- 2106225
- PAR ID:
- 10441850
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials
- Volume:
- 35
- Issue:
- 37
- ISSN:
- 0935-9648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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